Robust Ligand-Based Modeling of the Biological Targets of Known Drugs

Abstract

Systematic annotation of the primary targets of roughly 1000 known therapeutics reveals that over 700 of these modulate approximately 85 biological targets. We report the results of three analyses. In the first analysis, drug/drug similarities and target/target similarities were computed on the basis of three-dimensional ligand structures. Drug pairs sharing a target had significantly higher similarity than drug pairs sharing no target. Also, target pairs with no overlap in annotated drug specificity shared lower similarity than target pairs with increasing overlap. Two-way agglomerative clusterings of drugs and targets were consistent with known pharmacology and suggestive that side effects and drug-drug interactions might be revealed by modeling many targets. In the second analysis, we constructed and tested ligand-based models of 22 diverse targets in virtual screens using a background of screening molecules. Greater than 100-fold enrichment of cognate versus random molecules was observed in 20/22 cases. In the third analysis, selectivity of the models was tested using a background of drug molecules, with selectivity of greater than 80-fold observed in 17/22 cases. Predicted activities derived from crossing drugs against modeled targets identified a number of known side effects, drug specificities, and drug-drug interactions that have a rational basis in molecular structure.

Computational Methods in Drug Discovery

“…These curves plot the number of true positive predictions on the y-axis versus the false-positive predictions on the x-axis. A random predictor would result in a plot of a line with a slope of 1, whereas curves with high initial slopes above this line represent increasing performance scores for the method tested (Cleves and Jain, 2006;Hristozov et al, 2007). Receiver operating characteristic curves are therefore analyzed by determining the area under the curve, positive predictive value-the ratio of true positives in a subset selected in a vHTS screen, or enrichment-a benchmark that normalizes positive predictive value by the background ratio of positives in the dataset.…”

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

“…The crystal structure of each target with PDB entry marked with an asterisk was used for the docking-based virtual screens. b The chemical structures of the actives used for testing the virtual screening methods are shown in Table S1 Figure 1) were used in this study: dataset I contains 990 non-active molecules for all the eight targets, which were constructed by using the ligand preparation method provided by Bissantz et al [38] , and dataset II consists of 1000 non-active molecules, which were constructed by Cleves et al [39] . The molecules in these two datasets do not overlap.…”

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

“…Examples include the similarity ensemble approach developed by Keiser et al [346], three-dimensional shape based approach for identifying diverse targets [358], structural similarities of molecular scaffolds to map drug promiscuity GPCRs by encoding both, ligand descriptors and protein pharmacophoric properties, in a low dimensional fingerprint for chemogenomic screening applications [172]. Text mining approaches [361] and semantic framework based methods [362] have been applied to map and predict polypharmacology using widely available biochemical information contained in publicly available resources.…”